CU Employee CULytics Founder

Credit unions, like many organizations, are collecting more data than ever before. To manage effectively and analyze this data, credit unions need a data warehousing solution that can handle large volumes of data, provide fast and flexible querying, and offer robust security and compliance features.

A typical data warehouse technology stack usually consists of the following components:

  1. Infrastructure: This includes the hardware and software resources needed to support the data warehouse, such as servers, storage devices, networking equipment, and operating systems. Infrastructure components can be located on-premises, in the cloud, or a combination of both.

  2. Storage: The storage component of a data warehouse is responsible for storing and managing the data. It includes data management tools, data models, and data storage systems such as relational databases, data lakes, and data warehouses. Data storage systems need to be scalable, reliable, and able to handle large volumes of data.

  3. Data Visualization: Data visualization tools help transform raw data into meaningful insights by displaying it in a visual format. This includes dashboards, charts, graphs, and other visual representations of data that make it easier to interpret and analyze. Data visualization tools are critical for making data accessible and understandable to business users.

  4. Data Analytics: Data analytics tools allow users to extract insights and patterns from the data. This includes data mining, machine learning, and other analytical techniques that help identify trends, make predictions, and support decision-making. Data analytics tools can be integrated with other components of the data warehouse technology stack to enable more advanced analysis and modeling.

 In this article, we explore some of the top data warehouse storage technologies for credit unions.

1. Amazon Redshift

Amazon Redshift is a cloud-based data warehousing platform that is optimized for complex queries and data processing tasks, and can handle large volumes of data efficiently. It is built on top of PostgreSQL, a popular open-source database, which provides a familiar SQL-based interface for data warehousing and management. Amazon Redshift integrates well with other AWS services, such as Amazon S3, AWS Glue, and AWS Data Pipeline, which makes it easy to move data into and out of Redshift. Additionally, Amazon Redshift allows users to customize certain aspects of the platform, such as cluster configurations and security settings, which can provide more flexibility for credit unions with specific needs.

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2. Azure Synapse

Azure Synapse is a cloud-based data warehousing platform that provides an integrated analytics service that brings together big data and data warehousing. It offers a unified experience for data prep, data management, and data warehousing. Azure Synapse has integrations with other Microsoft Azure services, making it easy to move data into and out of the platform. It also provides advanced security features, including data encryption, user access controls, and threat detection.

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3. Cinchy

Cinchy is a data collaboration platform that focuses on enabling the secure and efficient sharing of data across organizations. Cinchy provides a unified data fabric that allows organizations to share data in real time without the need for data duplication or data movement. Cinchy is designed to be highly scalable and can handle large volumes of data. Cinchy offers a visual interface for data modeling and management and provides features for data governance and security.

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4. Databricks

Databricks is primarily a data processing and analysis platform that focuses on data engineering and data science workflows. It provides a unified workspace for data analysts, data engineers, and data scientists to collaborate on data processing, analysis, and modeling. It is built on top of Apache Spark, a fast and scalable data processing engine that can handle large-scale data processing tasks. It offers support for a wide range of programming languages, including Python, R, SQL, and Scala.

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5. Google BigQuery

Google BigQuery is a cloud-based data warehousing platform that provides a serverless infrastructure for running ad-hoc SQL queries on large datasets. It is designed to be highly scalable and can handle large-scale data processing tasks, and it can integrate with a wide range of other Google Cloud Platform services. It is built on top of Google's infrastructure, which provides fast and reliable data processing and analysis. It offers a range of security features, such as encryption, user access controls, and multi-factor authentication.

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6. Snowflake

Snowflake is a cloud-based data warehousing platform that provides a fully-managed, scalable, and secure data warehouse that can handle structured and semi-structured data. Snowflake offers a simple, SQL-based interface for data warehousing and management, and has integrations with a wide range of data visualization and analysis tools. Snowflake separates storage and compute, which allows credit unions to scale compute resources independently of storage resources, leading to cost savings. Additionally, Snowflake provides a range of security features to protect data, including multi-factor authentication, data encryption, and user access controls.

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In conclusion, credit unions have several options for data warehouse storage technologies. Each of these platforms provides a scalable, secure, and flexible data warehousing solution that can handle large volumes of data and provide fast and efficient querying. Ultimately, the choice of data warehouse storage technology will depend on the specific needs and requirements of each credit union.

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